Background: The context is the new Texas STAAR end-of-course testing program. Purpose: The authors developed a logistic regression model to predict who would pass-or-fail the new Texas chemistry STAAR end-of-course exam. Setting: Robert E. Lee High School (5A) with an enrollment of 2700 students, Tyler, Texas. Date of the study was the 2011-2012 academic year. Study Sample: A sample of n = 100 students from the author's chemistry classes (32 high school sophomores and 68 high school juniors). Intervention: Developed a binary logistic regression prediction model (no control group applicable). Research Design: Statistical Modeling. Control or Comparison Condition: Control or comparison group--not applicable for the study. Data Collection and Analysis: The students' (n = 100) STAAR test scores from the new Texas end-of-course chemistry pilot test were analyzed in the 2011-2012 school year. Variables included in the logistic regression model were as follows: Students' previous years science TAKS test scores (raw data); science TAKS scores and STAAR end-of-course scores coded pass (1) or fail (0) as categorical variables; and students' grade level coded sophomore (0) or junior (1) as categorical variables. Findings: A binary logistic regression analysis was performed using the new Texas end-of-course pilot chemistry STAAR test scores as the dependent variable (DV) and the previous year's science TAKS scores and grade level as predictor variables. A total of n = 100 cases were analyzed, and the full model was significantly reliable (chi-square = 102.568, df = 2, p less than 0.000). This model accounted for between 64.1% and 85.9% of the variance in STAAR status, with 92.9% of the students passing the STAAR test successfully predicted and 93.2% of students failing the STAAR test successfully predicted. Overall, 93.0% of the predictions were correct. The Wald statistic showed that the TAKS raw score reliably predicted passing or failing the STAAR end-of-course chemistry test. Conclusion: The binary logistic regression model was significantly reliable (chi-square = 102.568, df = 2, p less than 0.000). Overall, 93% of the predictions were correct. The model had a very high predictive outcome. Logistic Regression Variables are appended to this document.